def _identifying_params(self) -> Mapping[str, Any]:
        return {
            **{"deployment_name": self.deployment_name},
            **super()._identifying_params,
        }
    @property
    def _invocation_params(self) -> Dict[str, Any]:
        return {**{"engine": self.deployment_name}, **super()._invocation_params}
[docs]class OpenAIChat(BaseLLM, BaseModel):
    """Wrapper around OpenAI Chat large language models.
    To use, you should have the ``openai`` python package installed, and the
    environment variable ``OPENAI_API_KEY`` set with your API key.
    Any parameters that are valid to be passed to the openai.create call can be passed
    in, even if not explicitly saved on this class.
    Example:
        .. code-block:: python
            from langchain.llms import OpenAIChat
            openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
    """
    client: Any  #: :meta private:
    model_name: str = "gpt-3.5-turbo"
    """Model name to use."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Holds any model parameters valid for `create` call not explicitly specified."""
    openai_api_key: Optional[str] = None
    max_retries: int = 6
    """Maximum number of retries to make when generating."""
    prefix_messages: List = Field(default_factory=list)
    """Series of messages for Chat input."""
    streaming: bool = False
    """Whether to stream the results or not."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.ignore
    @root_validator(pre=True)